Navigating API Downtimes: A Case for Model Fallbacks

In 2023, OpenAI experienced downtimes on an astonishing 46 days. Fast forward to the first quarter of 2024, and users faced API downtimes on 9 days — roughly 10% of the time.

Before we start pointing fingers, it's worth noting that OpenAI isn't alone on this bumpy ride. Other providers, like Anthropic, had their share of rough patches. It's a common sight in our logs, reminiscent of how keeping a service up and running during a hype period can feel like balancing on a tightrope.

Here's the catch: these frequent outages are more than just annoying hiccups; they're serious disruptors for production-level LLM applications. You don't want the scenario where your customers are disgruntled simply because a provider went dark again.

So, how can we mitigate this? The answer is implementing model fallbacks. In our vast digital ecosystem, there are plenty of remarkable models waiting in the wings. The trick is to catch those response errors and deftly switch to an alternative model when your primary choice decides to take a nap.

Let me give you a pro tip: you can utilize tools like airouter.io to intelligently and automatically fallback to the next best model, ensuring that your application runs smoothly and consistently taps into a wide variety of awesome models.

In a world of fluctuating digital weather, thinking ahead can save you and your customers from getting caught in the rain. Model fallbacks might just be the umbrella you need.